Machining performance of TiO2 embedded-glass fiber reinforced composites with snake optimizer
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F24%3A00011994" target="_blank" >RIV/46747885:24210/24:00011994 - isvavai.cz</a>
Alternative codes found
RIV/46747885:24620/24:00011994
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0263224124001374?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0263224124001374?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.measurement.2024.114253" target="_blank" >10.1016/j.measurement.2024.114253</a>
Alternative languages
Result language
angličtina
Original language name
Machining performance of TiO2 embedded-glass fiber reinforced composites with snake optimizer
Original language description
In this study, nano titanium dioxide-filled glass fiber reinforced polymer composites (nTiO2-GFRPC) are developed, and their surface roughness and machinability (cutting force) performance are evaluated with a newly evolved metaheuristic snake optimizer. A hybrid grey theory-snake optimizer (GT-SO) algorithm is developed where grey theory combines output responses (surface roughness and cutting force) into a single objective function, and the snake optimizer finds the optimal results. The novelty of this study is the compatibility of two different varieties of machine learning algorithms into one and the combination of two different responses (surface roughness and cutting force) into a single objective function. Process variables (nanoparticles amount, fiber volume fraction and feed rate), their interaction and their influence are designed by Taguchi orthogonal array and their optimization is performed by GT-SO. The optimal results are achieved with 5 % TiO2 (Weight %), 20 % fiber volume fraction and 75 mm/min feed rate. The optimum surface roughness and cutting force results were 1.49 μm and 1332.93 N, respectively. The validation of results shows that the output performance improved from 0.8929 to 0.9712, indicating the performance of the developed GT-SO with an 8.06 % error. The developed method was compared with other metaheuristics algorithms to reveal its potential for adaptation in composite material‘s cutting, milling, shaping and other machining characteristics. The results also confirm that TiO2 amount is a highly influencing factor for surface roughness calculations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
21100 - Other engineering and technologies
Result continuities
Project
<a href="/en/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MEASUREMENT
ISSN
0263-2241
e-ISSN
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Volume of the periodical
227
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
1-16
UT code for WoS article
001182593400001
EID of the result in the Scopus database
2-s2.0-85184516764